Data processor, data processing system, and computer-readable recording medium

ABSTRACT

A data processor includes an obtainment part that obtains an image of a person&#39;s face, a creation part that creates a face direction map in which face images of the person facing respective directions are arranged, based on the image of the person&#39;s face obtained by the obtainment part, and a determination part that determines movement of the person&#39;s face, based on the face direction map and a moving image of the person&#39;s face obtained by the obtainment part.

FIELD OF THE INVENTION

The present invention relates to data processors, data processingsystems, and programs, and more particularly, to a data processor, adata processing system and a program for determination of movement of aperson's face based on an image of the person's face.

DESCRIPTION OF THE BACKGROUND ART

Patent Literature 1 below, for example, discloses a technique forextracting facial feature points such as eyes, a nose, and a mouth froman image of a person's face to determine which direction the person isfacing based on the positions of such feature points.

CITATION LIST Patent Literature

Patent Literature 1 JP2004-236186A

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

A method to determine which direction the person is facing based on thepositions of the feature points, however, results in a vast amount ofcomputing and increases processing load, and moreover, may determine adirection of a face incorrectly when, for example, a person is facingjust beside, since feature points cannot be extracted accurately.

The present invention has been made in view of such situation, and isdirected to obtaining a data processor, a data processing system, and aprogram that achieve determination of a direction of a face with reducedprocessing load and improved accuracy, in comparison with determinationof a direction of a face based on feature points.

Means to Solve the Problems

A data processor according to a first aspect of the present inventionincludes obtainment means for obtaining an image of a person's face,creation means for creating a face direction map in which face images ofthe person facing respective directions are arranged, based on the imageof the person's face obtained by the obtainment means, and determinationmeans for determining movement of the person's face, based on the facedirection map and a moving image of the person's face obtained by theobtainment means.

According to the data processor of the first aspect, the obtainmentmeans obtains an image of a person's face, and the creation meanscreates a face direction map based on the image of the person's faceobtained by the obtainment means. Then the determination meansdetermines movement of the person's face, based on the face directionmap and a moving image of the person's face obtained by the obtainmentmeans. In this way, a face direction map of a person is created inadvance, so as to enable the determination means to determine movementof a face by a simple process such as matching between the taken imageand the images in the face direction map. Thus processing load isreduced, in comparison with determination of a direction of a face basedon feature points. Moreover, the face direction map allows recording ofa profile, which enables accurate determination of a profile even when aperson is facing just beside. Accuracy of determination of a directionof a face is therefore improved, in comparison with determination of adirection of a face based on feature points.

A data processor according to a second aspect of the present inventionis the data processor according to the first aspect, wherein thecreation means estimates a three dimensional image of the person's facebased on a two dimensional image of the person's face obtained by theobtainment means, and creates the face direction map based on the threedimensional image.

In the data processor according to the second aspect, the creation meansestimates a three dimensional image of a person's face based on a twodimensional image of the person's face obtained by the obtainment means,and creates the face direction map based on the three dimensional image.Thus there is no need for a dedicated device for taking a threedimensional image of the person's face, with a general CCD camera fortaking a two dimensional image being sufficient, which simplifies thedevice configuration and reduces product cost. Moreover, there is noneed for taking face images from multiple directions to create the facedirection map, which improves convenience of a user.

A data processor according to a third aspect of the present invention isthe data processor according to the second aspect, wherein the creationmeans estimates the three dimensional image with a neural network.

In the data processor according to the third aspect, the creation meansestimates the three dimensional image from the two dimensional imagewith a neural network. This achieves simplified estimation of a threedimensional image.

A data processor according to a fourth aspect of the present inventionis the data processor according to the first aspect, wherein thecreation means creates the face direction map based on a threedimensional image of the person's face obtained by the obtainment means.

In the data processor according to the fourth aspect, the creation meanscreates the face direction map based on a three dimensional image of theperson's face obtained by the obtainment means. In this way, the facedirection map of a person is created by taking a three dimensional imageof the person, so as to improve accuracy in comparison with estimationof a three dimensional image from a two dimensional image. Moreover,there is no need for taking face images from multiple directions tocreate the face direction map, which improves convenience of a user.

A data processor according to a fifth aspect of the present invention isthe data processor according to any one of the second to fourth aspects,wherein the creation means gradually rotates the three dimensional imagein multiple directions to create an image of the person's face in eachdirection.

In the data processor according to the fifth aspect, the creation meansgradually rotates the three dimensional image in multiple directions tocreate an image of a person's face in each direction. This achievessimplified creation of the face direction map.

A data processor according to a sixth aspect of the present invention isthe data processor according to any one of the second to fourth aspects,wherein the creation means rotates the three dimensional image inmultiple specific directions to create an image of the person's face ineach specific direction, and creates an intermediate images betweenmultiple images in the multiple specific directions with a neuralnetwork.

In the data processor according to the sixth aspect, the creation meansrotates the three dimensional image in multiple specific directions tocreate an image of the person's face in each specific direction andcreates an intermediate image between multiple images in multiplespecific directions with a neural network. This achieves simplifiedcreation of the face direction map.

A data processing system according to a seventh aspect of the presentinvention includes an image taking device and a data processor. The dataprocessor includes obtainment means for obtaining an image of a person'sface from the image taking device, creation means for creating a facedirection map in which face images of the person facing respectivedirections are arranged, based on the image of the person's faceobtained by the obtainment means, and determination means fordetermining movement of the person's face, based on the face directionmap and a moving image of the person's face obtained from the imagetaking device by the obtainment means.

According to the data processing system of the seventh aspect, theobtainment means obtains an image of a person's face from the imagetaking device, and the creation means creates a face direction map basedon the image of the person's face obtained by the obtainment means. Thenthe determination means determines movement of the person's face, basedon the face direction map and a moving image of the person's faceobtained by the obtainment means. In this way, a face direction map of aperson is created in advance, so as to enable the determination means todetermine movement of a face by a simple process such as matchingbetween the taken image and the images in the face direction map. Thusprocessing load is reduced, in comparison with determination of adirection of a face based on feature points. Moreover, the facedirection map allows recording of a profile, which enables accuratedetermination of a profile even when a person is facing just beside.Accuracy of determination of a direction of a face is thereforeimproved, in comparison with determination of a direction of a facebased on feature points.

A program according to an eighth aspect of the present invention is aprogram for causing a computer installed in a data processor to functionas obtainment means for obtaining an image of a person's face, creationmeans for creating a face direction map in which face images of theperson's face facing respective directions are arranged, based on theimage of the person's face obtained by the obtainment means, anddetermination means for determining movement of the person's face, basedon the face direction map and a moving image of the person's faceobtained by the obtainment means.

According to the program of the eighth aspect, the obtainment meansobtains an image of a person's face, and the creation means creates aface direction map based on the image of the person's face obtained bythe obtainment means. Then the determination means determines movementof the person's face, based on the face direction map and a moving imageof the person's face obtained by the obtainment means. In this way, aface direction map of a person is created in advance, so as to enablethe determination means to determine movement of a face by a simpleprocess such as matching between the taken image and the images in theface direction map. Thus processing load is reduced, in comparison withdetermination of a direction of a face based on feature points.Moreover, the face direction map allows recording of a profile, whichenables accurate determination of a profile even when a person is facingjust beside. Accuracy of determination of a direction of a face istherefore improved, in comparison with determination of a direction of aface based on feature points.

Effects of the Invention

The present invention achieves determination of a direction of a facewith reduced processing load and improved accuracy, in comparison withdetermination of a direction of a face based on feature points.

These and other objects, features, aspects and advantages of the presentinvention will become more apparent from the following detaileddescription of the present invention when taken in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating a laptop personal computer asan example of a data processing system according to an embodiment of thepresent invention.

FIG. 2 is a diagram illustrating a hardware configuration of thepersonal computer.

FIG. 3 is a block diagram illustrating a function implemented by a dataprocessing part by performing a program.

FIG. 4 is a diagram illustrating a configuration of a creation part.

FIG. 5 is a diagram illustrating an example of a face direction map.

FIG. 6 is a diagram illustrating a first modification of the creationpart.

FIG. 7 is a diagram illustrating a second modification of the creationpart.

FIG. 8 is a diagram illustrating a third modification of the creationpart.

DESCRIPTION OF THE INVENTION

Preferred embodiments of the present invention are described in detailbelow referring to the drawings. It should be noted that identicalreference numerals throughout the drawings indicate identical orequivalent elements.

FIG. 1 is a schematic diagram illustrating a laptop personal computer 1as an example of a data processing system according to an embodiment ofthe present invention. FIG. 2 is a diagram illustrating a hardwareconfiguration of the personal computer 1. As illustrated in FIGS. 1 and2, the personal computer 1 includes an image taking part 11 such as aCCD camera, a data processing part 12 such as a CPU, a communicationpart 13 that communicates with an IP network or the like, a storage part14 such as a hard disk drive and a semiconductor memory, a display part15 such as a liquid crystal display, and an input part 16 such as akeyboard and a mouse. These devices are connected to each other via abus 11. The storage part 14 stores a program 20.

FIG. 3 is a block diagram illustrating a function implemented by thedata processing part 12 by performing the program 20. As illustrated inFIG. 3, the data processing part 12 includes an obtainment part 31, anextraction part 32, a creation part 33, a determination part 34, and animage processing part 35. In other words, the program 20 is a program tocause the data processing part 12 as a data processor to function as theobtainment part 31, the extraction part 32, the creation part 33, thedetermination part 34, and the image processing part 35.

The image taking part 11 takes a two dimensional image including auser's face.

The obtainment part 31 receives an input of image data D1 including theuser's face from the image taking part 11. The image data D1 can beobtained by, for example, taking an image of the user's face from thefront by the image taking part 11.

The extraction part 32 receives an input of the image data D1 from theobtainment part 31. The extraction part 32 extracts the user's face fromthe image data D1 by a well-known method such as binarization, edgeextraction, and affine transformation. The extraction part 32 alsogenerates image data D2 by compressing the image of the extracted faceto a predetermined size available as an input image to a neural network.

The creation part 33 receives an input of the image data D2 of a user'sface from the extraction part 32. The creation part 33 creates a facedirection map 40 of the user based on the image data D2. The facedirection map 40 is a map in which images of a user's face facingrespective directions are arranged. In other words, the face directionmap 40 includes images of a user's face taken from multiple directions.

FIG. 4 is a diagram illustrating a configuration of the creation part33. As illustrated in FIG. 4, the creation part 33 includes a multilayerneural network 51, a rotation part 52, and a Kohonen neural network 53.

The multilayer neural network 51 receives an input of the image data D2,which is a two dimensional image of the user's face, from the extractionpart 32. The multilayer neural network 51 estimates a three dimensionalimage based on the image data D2 to generate an image data D10, which isa three dimensional image of the user's face. The multilayer neuralnetwork 51 includes an input layer having multiple input units, anintermediate layer having multiple intermediate units, and an outputlayer having multiple output units. Appropriate weights, obtained fromlearning in advance, are assigned to each of the connections betweeneach input unit and each intermediate unit, and between eachintermediate unit and each output unit. In the learning, for example, atwo dimensional image of a person's face is taken with a CCD camera anda range image of the person's face is obtained with a laser rangefinder. Then the two dimensional image taken with the CCD camera is usedas an input image, and the range image obtained with the laser rangefinder is used as an instruction signal. By Learning multiple times withvarious people's faces, respective weights between each of the aboveunits are set so that an appropriate three dimensional image is obtainedfrom a two dimensional image of an arbitrary face.

The rotation part 52 receives an input of the image data D10 from themultilayer neural network 51. The rotation part 52 rotates the threedimensional image of the user's face, so as to create multiple twodimensional images of the user's face taken from a specific direction(hereinafter, “specific direction image”). For example, specificdirection images facing the front, to the right, to the left, up, down,to the upper right, to the upper left, to the lower right, and to thelower left are created.

The Kohonen neural network 53 receives an input of the image data D11 ofthe specific direction images created by the rotation part 52. TheKohonen neural network 53 creates the face direction map 40 based on theimage data D11. The Kohonen neural network 53 includes an input layerhaving multiple input units and an output layer having multiple outputunits. The specific direction images are inputted to the input layer.When a certain specific direction image is inputted to the input layer,this specific direction image is recorded in a certain specific outputunit among all of the output units and in multiple output units within apredetermined learning radius around this specific output unit. Whenanother specific direction image is inputted to the input layer, thisspecific direction image is recorded in another specific output unit andin multiple output units within a learning radius around this specificoutput unit. This process is performed on all of the specific directionimages. In a portion where recorded regions of the specific directionimages overlap each other, an intermediate image in which these specificdirection images are integrated (or mixed) with each other is recorded.For example, in a portion where recorded regions of a face image facingthe front and a face image facing to the right overlap each other, anintermediate image between the front and the right (image facingslightly to the right) is recorded. In this way, the Kohonen neuralnetwork 53 creates an intermediate image between multiple specificdirection images, so as to create a face direction map 40, which is aself-organizing map.

FIG. 5 is a diagram illustrating an example of the face direction map40. The face direction map 40 is configured as an aggregate of themultiple output units 100 of the Kohonen neural network 53, each of theoutput units 100 indicating one face image of a user. In the regions R1,R2, R3, R4, R5, R6, R7, R8, and R9, face images of a user facing to theupper right, up, to the upper left, to the right, the front, to theleft, to the lower right, down, and to the lower left are respectivelyrecorded. In the face direction map 40, the face is facing upper in anupper side of the map, facing further down in the lower side, facingfurther to the right in the left side, and facing further to the left inthe right side.

Referring to FIG. 3, the creation part 33 stores the face direction map40 so created as described above in the storage part 14.

Operation after creation of the face direction map 40 is completed isnext described. Description is given below of an example in which a useruses the personal computer 1 to access the Internet, so as to conductavatar chat. In the avatar chat, a character called an avatar isallotted to each user to make such a presentation that when a userinputs a sentence with the personal computer 1, the avatar speaks thesentence with a speech balloon or the like, and then avatars communicatewith each other in a virtual space.

While the user is conducting avatar chat, the image taking part 11 takesa two dimensional moving image including the user's face. The obtainmentpart 31 receives an input of moving image data D4 including the user'sface from the image taking part 11. The extraction part 32 receives aninput of the moving image data D4 from the obtainment part 31. Theextraction part 32 employs a well-known method such as binarization,edge extraction, and affine transformation to extract the user's facefrom the moving image data D4.

The determination part 34 receives an input of moving image data D5 ofthe user's face from the extraction part 32. The determination part 34determines movement of the user's face (nodding, head-shaking) based onthe face direction map 40 read from the storage part 14 and the movingimage data D5 inputted from the extraction part 32. Specifically, thedetermination part 34 searches all the face images included in the facedirection map 40 for a face image having the highest similarity bypattern matching or the like, with respect to face images in each of theframes in the moving image data D5. Then if the portions identified asthe face image having the highest similarity are tracked like regionsR5→R8→R5, the movement of the user's face is determined to be nodding.In contrast, if the portions identified as the face image having thehighest similarity are tracked like regions R5→R6→R5→R4→R5 or regionsR5→R4→R5→R6→R5), the movement of the user's face is determined to behead-shaking.

The image processing part 35 receives an input of data D6 indicating themovement of the user's face from the determination part 34. The imageprocessing part 35 outputs image data D7 that indicates an avatar makinga nodding movement if the movement of the user's face is nodding, whileit outputs image data D7 that indicates an avatar making a head-shakingmovement if the movement of the user's face is head-shaking. The imagedata D7 is inputted to the display part 15, and the display part 15presents avatar's movement. The image data D7 is also transmitted to aperson with whom the user is chatting via the communication part 13illustrated in FIG. 2, so that the avatar's movement is presented on thedisplay of the person. It should be noted that as the movement of theuser's face, not only nodding and head-shaking but also other movementssuch as head-tilting may be detected and incorporated in presentation ofthe avatar.

FIG. 6 is a diagram illustrating a first modification of the creationpart 33. The creation part 33 according to the present modificationincludes the multilayer neural network 51 and the rotation part 52.

The multilayer neural network 51 generates the image data D10, which isa three dimensional image of the user's face, in the same method as theabove embodiment.

The rotation part 52 receives an input of the image data D10 from themultilayer neural network 51. The rotation part 52 gradually rotates thethree dimensional image by a predetermined angle (for example, by a fewdegrees) from side to side and up and down, so as to create a twodimensional image of the user's face in each of the rotated position.Then the created two dimensional images are arranged on a map to createthe face direction map 40.

FIG. 7 is a diagram illustrating a second modification of the creationpart 33. The creation part 33 according to the present modificationincludes the rotation part 52 and the Kohonen neural network 53.

The rotation part 52 receives an input of the image data D10, which is athree dimensional image of the user's face. The three dimensional imageof the user's face can be created by an arbitrary three dimensionalmeasuring technique, and is obtained by the obtainment part 31.

For example, a vertically-striped color pattern of red, blue, and greenwith phases being shifted for each color is applied to a user from thefront, and the image of the color pattern projected on the user is takenwith a color camera placed diagonally to the front of the user.

Since the color pattern deforms with concavity and convexity of theuser's face, analyzing the deformation enables creation of a threedimensional image of the user's face.

For another example, a time-of-flight (TOF) camera is employed toirradiate a user with an infrared ray and detect the infrared rayreflected from the user with a sensor of the TOF camera. Since the timethe infrared ray takes (time of flight) to travel from the TOF camera tothe user and back to the TOF camera differs depending on concavity andconvexity of the user's face, analyzing the distribution of the time offlight enables creation of a three dimensional image of the user's face.

For another example, use of a stereo camera to take an image of a userenables creation of a three dimensional image of the user's face.

The rotation part 52 generates the image data D11 of the specificdirection images in the same method as the above embodiment.

The Kohonen neural network 53 creates the face direction map 40 based onthe image data D11, in the same method as the above embodiment.

FIG. 8 is a diagram illustrating a third modification of the creationpart 33. The creation part 33 according to the present modificationincludes the rotation part 52.

The rotation part 52 receives an input of the image data D10, which is athree dimensional image of the user's face. The image data D10 is imagedata created by an arbitrary three dimensional measuring technique,similar to the above second modification. The rotation part 52 createsthe face direction map 40 in the same method as the above firstmodification.

It should be noted that the above description is given of detectingmovement of a user's face to incorporate to movement of an avatar, onlyas a non-limiting example of a use of the present invention. Forexample, use for an interactive robot that performs communication with auser may be possible, so that the robot detects the movement of theuser's face and interprets the user's intention such as “yes” or “no”based on the detection.

As described above, according to the data processor of the presentembodiment, the obtainment part 31 obtains an image of a person's face,and the creation part 33 creates the face direction map 40 based on theimage of the person's face obtained by the obtainment part 31. Then thedetermination part 34 determines the movement of the person's face,based on the face direction map 40 and the moving image of the person'sface obtained by the obtainment part 31. In this way, the face directionmap 40 of a person is created in advance, so as to enable thedetermination part 34 to determine movement of a face by a simpleprocess such as matching between the taken image and the images in theface direction map 40. Thus processing load is reduced, in comparisonwith determination of a direction of a face based on feature points.Moreover, the face direction map allows recording of a profile, whichenables accurate determination of a profile even when a person is facingjust beside. Accuracy of determination of a direction of a face istherefore improved, in comparison with determination of a direction of aface based on feature points.

Also according to the data processor of the present embodiment, thecreation part 33 estimates a three dimensional image of a person's facebased on a two dimensional image of the person's face obtained by theobtainment part 31, and creates the face direction map 40 based on thethree dimensional image. Thus there is no need for a dedicated devicefor taking a three dimensional image of the person's face, with ageneral CCD camera for taking a two dimensional image being sufficient,which simplifies the device configuration and reduces product cost.Moreover, there is no need for taking face images from multipledirections to create the face direction map 40, which improvesconvenience of a user.

Also according to the data processor of the present embodiment, thecreation part 33 illustrated in FIGS. 4 and 6 estimates a threedimensional image from a two dimensional image with the multilayerneural network 51. This achieves simplified estimation of a threedimensional image.

Also according to the data processor of the present embodiment, thecreation part 33 illustrated in FIGS. 7 and 8 creates the face directionmap 40 based on a three dimensional image of a person's face obtained bythe obtainment part 31. In this way, the face direction map 40 of theperson is created by taking a three dimensional image of the person, soas to improve accuracy in comparison with estimation of a threedimensional image from a two dimensional image. Moreover, there is noneed for taking face images from multiple directions to create the facedirection map 40, which improves convenience of a user.

According to the data processor of the present embodiment, the creationpart 33 illustrated in FIGS. 6 and 8 gradually rotates the threedimensional image in multiple directions, so as to create an image of aperson's face in each direction. This achieves simplified creation ofthe face direction map 40.

According to the data processor of the present embodiment, the creationpart 33 illustrated in FIGS. 4 and 7 rotates the three dimensional imagein multiple specific directions, so as to create an image of a person'sface in each of the specific directions and creates an intermediateimage between multiple specific direction images with the Kohonen neuralnetwork 53. This achieves simplified creation of the face direction map40.

While the invention has been described in detail, the foregoingdescription is in all aspects illustrative and not restrictive. It istherefore understood that numerous modifications and variations can bedevised without departing from the scope.

EXPLANATION OF REFERENCE NUMERALS

1 personal computer

11 image taking part

12 data processing part

14 storage part

20 program

31 obtainment part

33 creation part

34 determination part

40 face direction map

51 multilayer neural network

53 Kohonen neural network

The invention claimed is:
 1. A data processor comprising: circuitryconfigured to: obtain an image of a person's face; create a facedirection map including a plurality of face images of the person facinga plurality of directions, the plurality of face images of the personfacing the plurality of directions being created based on a sameobtained image of the person's face; and determine a type of movement ofthe person's face by tracking person a direction of the person's face ina specific order using the plurality of face images within the facedirection map, wherein the specific order includes at least two faceimages of the plurality of face images, each of the at least two faceimage having a highest similarity with an obtained moving image of theperson's face.
 2. The data processor according to claim 1, wherein thecircuitry is configured to estimate a three dimensional image of theperson's face based on an obtained two dimensional image of the person'sface and to create the face direction map based on the three dimensionalimage.
 3. The data processor according to claim 2, wherein the circuitryis configured to estimate the three dimensional image with a neuralnetwork.
 4. The data processor according to claim 3, wherein thecircuitry is configured to gradually rotate the three dimensional imagein the plurality of directions to create a respective image of theperson's face in each direction.
 5. The data processor according toclaim 3, wherein the circuitry is configured to rotate the threedimensional image in a plurality of specific directions to create arespective image of the person's face in each specific direction, and tocreate an intermediate image between a plurality of images in theplurality of specific directions with a neural network.
 6. The dataprocessor according to claim 2, wherein the circuitry is configured togradually rotate the three dimensional image in the plurality ofdirections to create a respective image of the person's face in eachdirection.
 7. The data processor according to claim 2, wherein thecircuitry is configured to rotate the three dimensional image in aplurality of specific directions to create a respective image of theperson's face in each specific direction, and to create an intermediateimage between a plurality of images in the plurality of specificdirections with a neural network.
 8. The data processor according toclaim 1, wherein the circuitry is configured to create the facedirection map based on an obtained three dimensional image of theperson's face.
 9. The data processor according to claim 8, wherein thecircuitry is configured to gradually rotate the three dimensional imagein the plurality of directions to create a respective image of theperson's face in each direction.
 10. The data processor according toclaim 8, wherein the circuitry is configured to rotate the threedimensional image in a plurality of specific directions to create arespective image of the person's face in each specific direction, and tocreate an intermediate image between a plurality of images in theplurality of specific directions with a neural network.
 11. A dataprocessing system comprising: an image taking device; and a dataprocessor, the data processor including circuitry configured to: obtainan image of a person's face from the image taking device; create a facedirection map including a plurality of face images of the person facinga plurality of directions, the plurality of face images of the personfacing the plurality of directions being created based on a sameobtained image of the person's face; and determine a type of movement ofthe person's face by tracking person a direction of the person's face ina specific order using the plurality of face images within the facedirection map, wherein the specific order includes at least two faceimages of the plurality of face images, each of the at least two faceimages having a highest similarity with an obtained moving image of theperson's face.
 12. A non-transitory computer-readable recording mediumrecording a program for causing a computer installed in a data processorto perform: obtaining an image of a person's face; creating a facedirection map including a plurality of face images of the person's facefacing a plurality of directions, the plurality of face images of theperson facing the plurality of directions being created based on a sameobtained image of the person's face; and determine a type of movement ofthe person's face by tracking person a direction of the person's face ina specific order using the plurality of face images within the facedirection map, wherein the specific order includes at least two faceimages of the plurality of face images, each of the at least two faceimages having a highest similarity with an obtained moving image of theperson's face.
 13. The data processor according to claim 1, wherein thecircuitry is configured to search the plurality of face images, includedin the face direction map, for a face image having the highestsimilarity with an image in each frame of the obtained moving image ofthe person's face.
 14. The data processor according to claim 13, whereinthe circuitry is configured to determine a set of face images, from theplurality of face images, with the highest similarity with the image ineach frame of the obtained moving image of the person's face, and todetermine the type of movement of the person's face based on thedetermined set of face images.